Abstract
While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic “trait” architecture that is independent of any given paradigm. We have previously proposed the use of “cross-paradigm connectivity (CPC)” to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain’s “trait” structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional “trait” networks and offer some methodological implications for future CPC studies.
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Braun, U., Plichta, M. M., Esslinger, C., Sauer, C., Haddad, L., Grimm, O., & Meyer-Lindenberg, A. (2012). Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures. Neuroimage, 59(2), 1404–1412. https://doi.org/10.1016/j.neuroimage.2011.08.044
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network: anatomy, function, and relevance to disease. Annals of the New York Academy of Sciences, 1124, 1–38. https://doi.org/10.1196/annals.1440.011
Bullmore, E. T., & Bassett, D. S. (2011). Brain graphs: graphical models of the human brain connectome. Annual Review of Clinical Psychology, 7, 113–140. https://doi.org/10.1146/annurev-clinpsy-040510-143934
Cao, H., Plichta, M. M., Schafer, A., Haddad, L., Grimm, O., Schneider, M., & Tost, H. (2014). Test-retest reliability of fMRI-based graph theoretical properties during working memory, emotion processing, and resting state. Neuroimage, 84, 888–900. https://doi.org/10.1016/j.neuroimage.2013.09.013
Cao, H., Harneit, A., Walter, H., Erk, S., Braun, U., Moessnang, C.,.. . Tost, H. (2017). The 5-HTTLPR polymorphism affects network-based functional connectivity in the visual-limbic system in healthy adults. Neuropsychopharmacology. https://doi.org/10.1038/npp.2017.121.
Cao, H., Chen, O. Y., Chung, Y., Forsyth, J. K., McEwen, S. C., Gee, D. G., & Cannon, T. D. (2018a). Cerebello-thalamo-cortical hyperconnectivity as a state-independent functional neural signature for psychosis prediction and characterization. Nature Communications, 9(1), 3836. https://doi.org/10.1038/s41467-018-06350-7
Cao H, McEwen SC, Forsyth JK, Gee DG, Bearden CE, Addington J, Cannon TD (2018b) Toward leveraging human connectomic data in large consortia: generalizability of fMRI-based brain graphs across sites, sessions, and paradigms. Cerebral Cortex.https://doi.org/10.1093/cercor/bhy032
Cao, H., Chung, Y., McEwen, S. C., Bearden, C. E., Addington, J., Goodyear, B., & Cannon, T. D. (2019a). Progressive reconfiguration of resting-state brain networks as psychosis develops: preliminary results from the North American Prodrome Longitudinal Study (NAPLS) consortium. Schizophrenia Research. https://doi.org/10.1016/j.schres.2019.01.017
Cao, H., Ingvar, M., Hultman, C. M., & Cannon, T. (2019b). Evidence for cerebello-thalamo-cortical hyperconnectivity as a heritable trait for schizophrenia. Translational Psychiatry, 9(1), 192. https://doi.org/10.1038/s41398-019-0531-5
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16(9), 1348–1355. https://doi.org/10.1038/nn.3470
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83(1), 238–251. https://doi.org/10.1016/j.neuron.2014.05.014
Cole, M. W., Repovs, G., & Anticevic, A. (2014). The frontoparietal control system: a central role in mental health. The Neuroscientist : a Review Journal Bringing Neurobiology, Neurology and Psychiatry, 20(6), 652–664. https://doi.org/10.1177/1073858414525995
Deuker, L., Bullmore, E. T., Smith, M., Christensen, S., Nathan, P. J., Rockstroh, B., & Bassett, D. S. (2009). Reproducibility of graph metrics of human brain functional networks. NeuroImage, 47(4), 1460–1468. https://doi.org/10.1016/j.neuroimage.2009.05.035
Dosenbach, N. U., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A., & Petersen, S. E. (2007). Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences of the United States of America, 104(26), 11073–11078. https://doi.org/10.1073/pnas.0704320104
Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A.,.. . Schlaggar, B. L. (2010). Prediction of individual brain maturity using fMRI. Science, 329(5997), 1358–1361. https://doi.org/10.1126/science.1194144.
Dubois, J., Galdi, P., Han, Y., Paul, L. K., & Adolphs, R. (2018). Resting-state functional brain connectivity best predicts the personality dimension of openness to experience. Personal Neuroscience, 1. https://doi.org/10.1017/pen.2018.8.
Elliott, M. L., Knodt, A. R., Cooke, M., Kim, M. J., Melzer, T. R., Keenan, R., & Hariri, A. R. (2019). General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage, 189, 516–532. https://doi.org/10.1016/j.neuroimage.2019.01.068
Fiecas, M., Ombao, H., van Lunen, D., Baumgartner, R., Coimbra, A., & Feng, D. (2013). Quantifying temporal correlations: a test-retest evaluation of functional connectivity in resting-state fMRI. Neuroimage, 65, 231–241. https://doi.org/10.1016/j.neuroimage.2012.09.052.
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., & Constable, R. T. (2015). Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671. https://doi.org/10.1038/nn.4135
Finn, E. S., Scheinost, D., Finn, D. M., Shen, X. L., Papademetris, X., & Constable, R. T. (2017). Can brain state be manipulated to emphasize individual differences in functional connectivity? NeuroImage, 160, 140–151. https://doi.org/10.1016/j.neuroimage.2017.03.064
First, M. B., Spitzer, R. L., Gibbon , M., & Williams, J. B. W. (2002). Structured clinical interview for DSM-IV-TR axis I disorders, research version, patient edition (SCID-I/P). New York: Biometrics Research, New York State Psychiatric Institute.
Forsyth, J. K., McEwen, S. C., Gee, D. G., Bearden, C. E., Addington, J., Goodyear, B.,.. . Cannon, T. D. (2014). Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: analysis from the North American Prodrome Longitudinal Study. Neuroimage, 97, 41–52. https://doi.org/10.1016/j.neuroimage.2014.04.027.
Geerligs, L., Rubinov, M., Cam, C., & Henson, R. N. (2015). State and trait components of functional connectivity: individual differences vary with mental state. The Journal of Neuroscience, 35(41), 13949–13961. https://doi.org/10.1523/JNEUROSCI.1324-15.2015
Hsu, W. T., Rosenberg, M. D., Scheinost, D., Constable, R. T., & Chun, M. M. (2018). Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Social Cognitive and Affective Neuroscience, 13(2), 224–232. https://doi.org/10.1093/scan/nsy002
Iidaka, T., Matsumoto, A., Nogawa, J., Yamamoto, Y., & Sadato, N. (2006). Frontoparietal network involved in successful retrieval from episodic memory. Spatial and temporal analyses using fMRI and ERP. Cerebral Cortex, 16(9), 1349–1360. https://doi.org/10.1093/cercor/bhl040
Jiang, R., Calhoun, V. D., Zuo, N., Lin, D., Li, J., Fan, L.,.. . Sui, J. (2018). Connectome-based individualized prediction of temperament trait scores. NeuroImage, 183, 366–374. https://doi.org/10.1016/j.neuroimage.2018.08.038.
Kaufmann, T., Alnaes, D., Doan, N. T., Brandt, C. L., Andreassen, O. A., & Westlye, L. T. (2017). Delayed stabilization and individualization in connectome development are related to psychiatric disorders. Nature Neuroscience, 20(4), 513–515. https://doi.org/10.1038/nn.4511
Krienen, F. M., Yeo, B. T., & Buckner, R. L. (2014). Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 369(1653). https://doi.org/10.1098/rstb.2013.0526.
Lakes, K. D., & Hoyt, W. T. (2009). Applications of generalizability theory to clinical child and adolescent psychology research. Journal of Clinical Child and Adolescent Psychology, 38(1), 144–165. https://doi.org/10.1080/15374410802575461
Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M. Y.,.. . Petersen, S. E. (2015). Functional system and areal organization of a highly sampled individual human brain. Neuron, 87(3), 657–670. https://doi.org/10.1016/j.neuron.2015.06.037.
Liang, X., Wang, J., Yan, C., Shu, N., Xu, K., Gong, G., & He, Y. (2012). Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: a resting-state functional MRI study. PLoS One, 7(3), e32766. https://doi.org/10.1371/journal.pone.0032766
Liao, X. H., Xia, M. R., Xu, T., Dai, Z. J., Cao, X. Y., Niu, H. J.,.. . He, Y. (2013). Functional brain hubs and their test-retest reliability: a multiband resting-state functional MRI study. Neuroimage, 83, 969–982. https://doi.org/10.1016/j.neuroimage.2013.07.058.
Lin, Q., Rosenberg, M. D., Yoo, K., Hsu, T. W., O’Connell, T. P., & Chun, M. M. (2018). Resting-state functional connectivity predicts cognitive impairment related to Alzheimer’s disease. Frontiers in Aging Neuroscience, 10, 94. https://doi.org/10.3389/fnagi.2018.00094
Lindquist, K. A., & Barrett, L. F. (2012). A functional architecture of the human brain: emerging insights from the science of emotion. Trends in Cognitive Sciences, 16(11), 533–540. https://doi.org/10.1016/j.tics.2012.09.005
McCarthy, G., Blamire, A. M., Puce, A., Nobre, A. C., Bloch, G., Hyder, F.,.. . Shulman, R. G. (1994). Functional magnetic resonance imaging of human prefrontal cortex activation during a spatial working memory task. Proc Natl Acad Sci U S A, 91(18), 8690–8694.
McGlashan, T. H., Miller, T. J., Woods, S. W., Hoffman, R. E., & Davidson, L. (2001). Instrument for the Assessment of Prodromal Symptoms and States. In T. Miller, S. A. Mednick, T. H. McGlashan, J. Libiger & J. O. Johannessen (Eds.), Early intervention in psychotic disorders (pp. 135–149). Dordrecht: Springer Netherlands.
Noble, S., Scheinost, D., Finn, E. S., Shen, X., Papademetris, X., McEwen, S. C.,.. . Constable, R. T. (2016). Multisite reliability of MR-based functional connectivity. Neuroimage. https://doi.org/10.1016/j.neuroimage.2016.10.020.
Owen, A. M., McMillan, K. M., Laird, A. R., & Bullmore, E. (2005). N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25(1), 46–59. https://doi.org/10.1002/hbm.20131
Plitt, M., Barnes, K. A., Wallace, G. L., Kenworthy, L., & Martin, A. (2015). Resting-state functional connectivity predicts longitudinal change in autistic traits and adaptive functioning in autism. Proceedings of the National Academy of Sciences of the United States of America, 112(48), E6699–E6706. https://doi.org/10.1073/pnas.1510098112
Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M. Y.,.. . Mumford, J. A. (2015). Long-term neural and physiological phenotyping of a single human. Nat Commun, 6, 8885. https://doi.org/10.1038/ncomms9885.
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A.,.. . Petersen, S. E. (2011). Functional network organization of the human brain. Neuron, 72(4), 665–678. https://doi.org/10.1016/j.neuron.2011.09.006.
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage, 84, 320–341. https://doi.org/10.1016/j.neuroimage.2013.08.048
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676–682. https://doi.org/10.1073/pnas.98.2.676
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19(1), 165–171. https://doi.org/10.1038/nn.4179
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E.,.. . Wolf, D. H. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage, 64, 240–256. https://doi.org/10.1016/j.neuroimage.2012.08.052.
Shavelson, R. J., & Webb, N. M. (1991). Generalizability theory: a primer. London: Sage.
Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N.,.. . Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273–289. https://doi.org/10.1006/nimg.2001.0978.
Wang, J. H., Zuo, X. N., Gohel, S., Milham, M. P., Biswal, B. B., & He, Y. (2011). Graph theoretical analysis of functional brain networks: test-retest evaluation on short- and long-term resting-state functional MRI data. PLoS One, 6(7), e21976. https://doi.org/10.1371/journal.pone.0021976.
Watson, C. E., Gotts, S. J., Martin, A., & Buxbaum, L. J. (2018). Bilateral functional connectivity at rest predicts apraxic symptoms after left hemisphere stroke. NeuroImage: Clinical. https://doi.org/10.1016/j.nicl.2018.08.033.
Wechsler, D. (1999). Wechsler abbreviated scale of intelligence. New York: Psychological Corporation.
Welton, T., Kent, D. A., Auer, D. P., & Dineen, R. A. (2015). Reproducibility of graph-theoretic brain network metrics: a systematic review. Brain Connectivity, 5(4), 193–202. https://doi.org/10.1089/brain.2014.0313
Zhang, C., Dougherty, C. C., Baum, S. A., White, T., & Michael, A. M. (2018). Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity. Human Brain Mapping, 39(4), 1765–1776. https://doi.org/10.1002/hbm.23950
Zuo, X. N., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J.,.. . Milham, M. P. (2014). An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data, 1, 140049. https://doi.org/10.1038/sdata.2014.49.
Funding
This work was supported by the Brain and Behavior Research Foundation NARSAD Young Investigator Grant (No. 27068) to Dr. Cao, by gifts from the Staglin Music Festival for Mental Health and International Mental Health Research Organization to Dr. Cannon, and by National Institute of Health (NIH) grants U01 MH081902 to Dr. Cannon, P50 MH066286 and the Miller Family Endowed Term Chair to Dr. Bearden, U01 MH081857 to Dr. Cornblatt, U01 MH82022 to Dr. Woods, U01 MH066134 to Dr. Addington, U01 MH081944 to Dr. Cadenhead, R01 U01 MH066069 to Dr. Perkins, R01 MH076989 to Dr. Mathalon, and U01 MH081988 to Dr. Walker.
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Dr. Cannon has served as a consultant for Boehringer-Ingelheim Pharmaceuticals and Lundbeck A/S. The other authors report no conflicts of interest.
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Cao, H., Chen, O.Y., McEwen, S.C. et al. Cross-paradigm connectivity: reliability, stability, and utility. Brain Imaging and Behavior 15, 614–629 (2021). https://doi.org/10.1007/s11682-020-00272-z
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DOI: https://doi.org/10.1007/s11682-020-00272-z